Data analysts play a critical role in empowering health systems to make changes that drive long-lasting improvement. With big data sets to collate, interpret, and translate, health systems rely on data analysts to transform numbers on a spreadsheet into meaningful insights that lead to change. In 2020, with the unexpected onset of COVID-19, the role of a data analyst has become increasingly fundamental to a health system’s ability to react quickly and effectively to the pandemic.
While data analysts play a vital role in enabling improvement within a health system, they do not always have the tools and support they need. For example, a 2018 survey of data analysts by data science company Kaggle shows they spend the majority of their time cleaning data rather than analyzing it. As a result of overwhelming data support needs—exacerbated by the pandemic due to increased data-sharing and emerging analytic needs—healthcare data analysts spend most of their time on tasks that don’t lead to real care transformation.
Data analysts have many responsibilities, including interpreting large data sets, creating graphs and models, and understanding interoperability. However, this often means data analysts are all-consumed with secondary tasks, making it difficult to focus on their primary goal—assisting organizational leaders in achieving optimal healthcare management.
To unlock data to drive ongoing, meaningful adjustments and quickly respond to market changes, data analysts can stay focused on top-of-license work with robust analytics tools to offload basic responsibilities. Sophisticated data and analytics tools can help cleanse dirty data, optimize data sharing and access, and centralize data into one place (e.g., the Health Catalyst Data Operating System (DOS™)). The value of analytics tools goes beyond the applications because it frees up analysts’ time to focus on improvement and more rapidly identify trends to drive business strategy.
Acquiring the following four skills, on top of having data and analytics support tools in place, will help data analysts perform at the top of their license, avoid getting bogged down by rudimentary tasks, and guide their organizations towards data-driven, meaningful improvement:
Smart healthcare organizations rely on healthcare analytics to guide care improvement and delivery decisions based on data insights. Throughout the process of transforming data into information, data analysts must rely on their foundational competencies to unleash the data within their organization’s analytics platform, a big investment for any health system.
Ensuring data analysts have six specific competencies allows healthcare organizations to generate a return on investment for their data platform and maximize the data within the analytics platform to direct the improvement-oriented changes:
After data analysts have mastered the foundational skills, knowledge, and experience to manage and analyze data, what takes them to the next level? Data analysts transition from good to great when they keep the end goal in mind throughout an entire project and derive objective information for decision makers along the way.
Data analysts can adopt two principles to begin with the end in mind:
Rising costs, an increasing focus on population health, alternative payment models, and COVID-19 are a few reasons why healthcare is anything but simple. To manage this complexity, digitization is no longer an option but an imperative. At the heart of a digitized health system lies a need for healthcare data analysts to understand the system’s pressures, so their analyses help leaders develop strategies to improve care delivery and keep facility doors open and profitable. Analysts forecast volume, build dashboards, and more, but their real value lies in their ability to operate with a problem-solving mindset.
Too often, low-level tasks (e.g., building dashboards, training other employees about new technologies, and generating one-off reports) encumber data analysts, leaving little time for problem solving. Although these tasks are part of the job, a data analyst must be proactive in freeing herself from focusing only on low-level work by continually asking herself if she is providing leaders with valuable information that drives change.
Data analysts must use data to understand a health system’s problems from an operational, clinical, and financial angle. With a comprehensive understanding of the problem, the data analyst is prepared to offer greater insights that lead to the right course of resolution.
As data becomes more abundant in healthcare, especially when organizations are emphasizing interoperability to learn more about COVID-19, so does the need for data detectives. A healthcare data analyst assumes the role of data detective by identifying new opportunities, determining how to pursue them, and leveraging data across all initiatives. The data detective pieces together real-world concepts within a database to build accurate representations of a health system’s patient population with data. Then, the data detective analyzes and identifies potential target areas for improvement within the data construct and couples this analysis with a subject matter expert’s viewpoint to decide the best course of action.
Donning the data detective hat includes data analysis, seeking expertise from team members, and critically thinking about possible outcomes or scenarios based on expansive information from across the system to solve small and large problems spanning the organization.
As health systems feel more pressure to improve outcomes with limited resources, respond to COVID-19, and generate revenue on thin margins, data analysts keep health systems on the right course and inform leaders when they take a wrong turn. If data analysts can avoid becoming all consumed with secondary tasks—such as data preparation and management—they can transform data into meaningful information. This information guides leaders to make sound financial and clinical decisions that allow the health system to reach peak operational efficiency, even in the dire times of a pandemic.
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